Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3742-3746,3765

Database index recommendation based on multi-thread parallel reinforcement learning

Niu Xiangyu1
You Jinguo1,2
Yu Wenbo1
1. Faculty of Information Engineering & Automation, Kunming University of Science & Technology, Kunming 650000, China
2. Yunnan Key Laboratory of Artificial Intelligence, Kunming 650000, China
DOI: 10.19734/j.issn.1001-3695.2023.03.0146

Abstract

Indexing is an important method to improve database performance. At present, with the development of reinforcement learning algorithm, there are a series of methods to solve the index recommendation problem by reinforcement learning. Aiming at the problem that the existing deep reinforcement learning index recommendation algorithm has long training time and unstable training, this paper proposed an index recommendation algorithm based on A2C(advantage actor-critical), called PRELIA(parallel compensation learning index advisor). In order to improve the accuracy and efficiency of index selection and reduce the occupation of index space, the algorithm added the characteristic matrix of the number of rows scanned by load index and normalized the reward value. Experimental results on different data sets show that the proposed algorithm can gua-rantee the index recommendation quality equivalent to that of the compared algorithms, while the recommended index occupies less storage space, and the training time is more than 4 times longer than that of the baseline algorithms.

Foundation Support

国家自然科学基金资助项目(62062046)
CCF信息系统开放资助项目(HZ2021F0055A)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.03.0146
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: Software Technology Research
Pages: 3742-3746,3765
Serial Number: 1001-3695(2023)12-034-3742-05

Publish History

[2023-07-05] Accepted Paper
[2023-12-05] Printed Article

Cite This Article

牛祥虞, 游进国, 虞文波. 基于多线程并行强化学习的数据库索引推荐 [J]. 计算机应用研究, 2023, 40 (12): 3742-3746,3765. (Niu Xiangyu, You Jinguo, Yu Wenbo. Database index recommendation based on multi-thread parallel reinforcement learning [J]. Application Research of Computers, 2023, 40 (12): 3742-3746,3765. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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